Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/15136
Title: Hardware security against IP piracy using secure fingerprint encrypted fused amino-acid biometric with facial anthropometric signature
Authors: Sengupta, Anirban
Anshul, Aditya
Singh, Ayush Kumar
Keywords: Amino acid biometric;Facial anthropometry;Hardware security;IP piracy;Secure fingerprint
Issue Date: 2025
Publisher: Elsevier B.V.
Citation: Sengupta, A., Anshul, A., & Singh, A. K. (2025). Hardware security against IP piracy using secure fingerprint encrypted fused amino-acid biometric with facial anthropometric signature. Microprocessors and Microsystems. Scopus. https://doi.org/10.1016/j.micpro.2024.105131
Abstract: In the era of modern global design supply chain, the emergence of hardware threats is on the rise. Conventional hardware security techniques may fall short in terms of offering inferior tamper tolerance, unpersuasive digital ownership proof and weaker entropy, for sturdy intellectual property (IP) piracy detection and seamless IP ownership conflict resolution process. This paper presents a novel hardware security methodology based on IP seller's amino acid biometric and facial anthropometric features to generate an encrypted fused signature using multi-key driven non-invertible fingerprint, for providing sturdy detective countermeasure against IP piracy. The proposed approach exploits AES framework, where the generated key-translated fingerprint minutiae points of the IP seller is used as an encryption key. The proposed methodology is highly robust against hardware threats as it capable to generate large size covert security constraints for embedding, as digital evidence, in the IP design during high level synthesis (HLS). The results of the proposed approach on comparison with existing approaches, indicates enhanced tamper tolerance ability (against brute force attack) of upto 1.15E+77, lower probability of coincidence or false positive (against ghost signature search attack) of upto 6.72E-06, and stronger entropy of upto 2.06E-138, respectively. © 2024 Elsevier B.V.
URI: https://doi.org/10.1016/j.micpro.2024.105131
https://dspace.iiti.ac.in/handle/123456789/15136
ISSN: 0141-9331
Type of Material: Journal Article
Appears in Collections:Department of Computer Science and Engineering

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